Passive mobile sensing and psychological traits for large scale mood prediction
Open accessAbstract
Experience sampling has long been the established method to sample people's mood in order to assess their mental state. Smartphones start to be used as experience sampling tools for mental health state as they accompany individuals during their day and can therefore gather in-the-moment data. However, the granularity of the data needs to be traded off with the level of interruption these tools introduce. As a consequence the data collected with this technique is often sparse. This has been obviated by the use of passive sensing in addition to mood reports, however, this adds additional noise.
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Recent work explored fine-grained mood prediction through regression and clustering, as well as novel multi-dimensional formulations of the Affect Grid through multi-task learning.
“Instead, recent work explored fine-grained mood prediction through regression and clustering [163], as well as novel multi-dimensional formulations of the Affect Grid through multi-task learning [164].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [163]
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- Authors
- Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J.
- Affiliations
- University of Cambridge (GB)
- University of Cambridge, UK Dept. of Comp. Sci. & Tech.
- University of Southampton (GB)
- University of Cambridge, UK Dept. of Psychology
- Venue
- Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare
- Publisher
- ACM
- Published
- 2019-05-20
- Pages
- 272-281
- ISSN
- 2153-1633 (ISSN-L)
- OpenAlex
- W2946726116
- Cited by
- 40
- References
- 49
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 6 · 2023: 10 · 2022: 6 · 2021: 7 · 2020: 4
- FWCI
- 5.36
- Type
- conference paper
- Language
- en
- Open access
- Locations
- International Conference on Pervasive Computing Technologies for Healthcare (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Apollo
- Funders
- Embiricos Trust, Jesus College, Cambridge
- Nokia Bell Labs
- Engineering and Physical Sciences Research Council (EP/I032673/1, EP/N509620/1)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mood prediction, ecological momentary assessment, smartphone sensing, psychological characteristics, mental health monitoring, data granularity, data sparsity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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